An AS-LS Algorithm by QR Factorization Based on Householder Reflections in an Approximation of a 1-Dimensional Decreasing Undamped Sinus Function
نویسنده
چکیده
1 1 Basics of Developing Regression Models from Data 3 1.1 Classic Regression Support Vector Machines Learning Setting 3 2 Active Set Method for Solving QP Based SVMs’ Learning 11 3 Active Set Least Squares (AS-LS) Regression 15 3.1 Implementation of the Active Set Least Squares Algorithm 19 3.1.1 Basics of Orthogonal Transformation 20 3.1.2 An Iterative Update of the QR Decomposition by Householder Reflection 21 3.2 An Active Set Least Squares with Weights Constraints – Bounded LS Problem 26 4 Comparisons of SVMs and AS-LS Regression 29 4.1 Performance of an Active Set Least Squares (AS-LS) without Constraints 32 4.2 Performance of a Bounded Active Set Least Squares (AS-BLS) with Constraints 33
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High Dimensional Function Approximation [ Regression, Hypersurface Fitting ] by an Active Set Least Squares Learning Algorithm
1 1 Basics of Developing Regression Models from Data 3 1.1 Classic Regression Support Vector Machines Learning Setting 3 2 Active Set Method for Solving QP Based SVMs’ Learning 11 3 Active Set Least Squares (AS-LS) Regression 15 3.1 Implementation of the Active Set Least Squares Algorithm 19 3.1.1 Basics of Orthogonal Transformation 20 3.1.2 An Iterative Update of the QR Decomposition by Househ...
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